ImmPort Ontology Conference: Difference between revisions

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1. What are the specific combinations of surface markers and internal proteins that reliably identify the same cell types? For instance CD19, B220, and the BCR (B cell receptor) are all found on B cells, and one can conceive of protocols that measure the presence of only one of these markers, yet give equal assurance that B cells are the cells being assayed. Can we push this paradigm further for more granular cell types?
1. What are the specific combinations of surface markers and internal proteins that reliably identify the same cell types? For instance CD19, B220, and the BCR (B cell receptor) are all found on B cells, and one can conceive of protocols that measure the presence of only one of these markers, yet give equal assurance that B cells are the cells being assayed. Can we push this paradigm further for more granular cell types?
1. What are the specific combinations of surface markers and internal proteins that reliably identify the same cell types?
For instance CD19, B220, and the BCR (B cell receptor) are all found on B cells, and one can conceive of protocols that measure
the presence of only one of these markers, yet give equal assurance that B cells are the cells being assayed.
Can we push this paradigm further for more granular cell types?
YP: I think we can skip this question, and instead rely on the cell definitions listed in the paper below, which is the basis of HIPC's classification.
This issue is addressed in https://bisc2012.atlassian.net/browse/OD-56


2. What surface markers or internal proteins have reliable associations with biological processes, such that when we see a novel cell type or a variant of a known cell type we can predict the cell's function or (in other words the GO:Biological Processes it is capable of carrying out or participating in)? This question can obviously leverage existing GO annotations for particular proteins, some of which already have co-annotation with CL terms. But it can also lead to new terms for GO:Biological Processes and for CL cell types.
2. What surface markers or internal proteins have reliable associations with biological processes, such that when we see a novel cell type or a variant of a known cell type we can predict the cell's function or (in other words the GO:Biological Processes it is capable of carrying out or participating in)? This question can obviously leverage existing GO annotations for particular proteins, some of which already have co-annotation with CL terms. But it can also lead to new terms for GO:Biological Processes and for CL cell types.
2. What surface markers or internal proteins have reliable associations with biological processes, such that when we see a novel cell type
or a variant of a known cell type we can predict the cell's function or (in other words the GO:Biological Processes it is capable of carrying out
or participating in)? This question can obviously leverage existing GO annotations for particular proteins, some of which already have co-annotation
with CL terms. But it can also lead to new terms for GO:Biological Processes and for CL cell types.
YP: I would drop this question in favor of keeping the focus on cell type classification. Much too ambitious, I believe.


3. How do we determine what is really a new cell type rather than either  a refinement of an existing cell type generated by additional markers, or (2) a transient activation state of some known cell type?
3. How do we determine what is really a new cell type rather than either  a refinement of an existing cell type generated by additional markers, or (2) a transient activation state of some known cell type?
 
3. How do we determine what is really a new cell type rather than either a refinement of an existing cell type generated by additional markers, or
(2) a transient activation state of some known cell type?
--> this should be the first question and the main focus of the Thur session ***
I would rephrase the question as something like this: Can we list a set of properties that distinguish a cell type from a transient state?
4. How can we leverage CyTOF to develop a true step-by-step picture of hematopoiesis? This is a question for both ontology and the experimental approach.
4. How can we leverage CyTOF to develop a true step-by-step picture of hematopoiesis? This is a question for both ontology and the experimental approach.
4. How can we leverage CyTOF to develop a true step-by-step picture of hematopoiesis? This is a question for both ontology and the experimental approach.
Too vague. How about: can we come up with an agreed process flow whereby at the end of the workflow we agree that a potential new cell type exists?





Revision as of 17:25, 1 July 2013

Where: Stanford University

When: September 4-5, 2013

Audience

  • Day 1 is intended for all those engaged in information-driven immunology research who have an interest in the work of ImmPort and/or in ontology and data standardization
  • Day 2 (by invitation only) is intended primarily for those interested in CyTOF and related issues of data management in immunological science.

Background resources

  • An overview of ontologies proposed by ImmPort for use across the immunology research community is provided here

If you are interested in attending please contact Barry Smith as soon as possible.

Wednesday, September 4, 2013

Goals

  • Work out with bench immunologists how nomenclature schemes can evolve to support enhanced discoverability and reusability through use of standards and ontologies
  • Provide arguments and success stories that will help to achieve buy-in from bench immunologists as to the importance of standards and ontologies
  • Provide examples of ontology content and of good practice use of ontologies which will help immunologists to rationalize their nomenclature and help them understand how ontologies are applied

Schedule

8:30 Registration and Continental Breakfast
9:00 What Benefits Can Ontology Bring to the DAIT Research Community?
Overview by Barry Smith
10:15 Break
10:30 ImmPort Ontologies
12:00 Lunch
13:00 Flow Cytometry
15:00 Break
15:30 Shai Shen-Orr: Ontology, NLP and the Semantic Enhancement of Immunology Research Literature
16:30 Lindsay Cowell: Immunology Ontology and NLP

Thursday, September 5, 2013

We will begin by going through the steps of the ontological process involved in handling CyTOF data in order to address the following

Major Questions for Discussion

1. What are the specific combinations of surface markers and internal proteins that reliably identify the same cell types? For instance CD19, B220, and the BCR (B cell receptor) are all found on B cells, and one can conceive of protocols that measure the presence of only one of these markers, yet give equal assurance that B cells are the cells being assayed. Can we push this paradigm further for more granular cell types?

1. What are the specific combinations of surface markers and internal proteins that reliably identify the same cell types? For instance CD19, B220, and the BCR (B cell receptor) are all found on B cells, and one can conceive of protocols that measure the presence of only one of these markers, yet give equal assurance that B cells are the cells being assayed. Can we push this paradigm further for more granular cell types? YP: I think we can skip this question, and instead rely on the cell definitions listed in the paper below, which is the basis of HIPC's classification. This issue is addressed in https://bisc2012.atlassian.net/browse/OD-56


2. What surface markers or internal proteins have reliable associations with biological processes, such that when we see a novel cell type or a variant of a known cell type we can predict the cell's function or (in other words the GO:Biological Processes it is capable of carrying out or participating in)? This question can obviously leverage existing GO annotations for particular proteins, some of which already have co-annotation with CL terms. But it can also lead to new terms for GO:Biological Processes and for CL cell types.

2. What surface markers or internal proteins have reliable associations with biological processes, such that when we see a novel cell type or a variant of a known cell type we can predict the cell's function or (in other words the GO:Biological Processes it is capable of carrying out or participating in)? This question can obviously leverage existing GO annotations for particular proteins, some of which already have co-annotation with CL terms. But it can also lead to new terms for GO:Biological Processes and for CL cell types. YP: I would drop this question in favor of keeping the focus on cell type classification. Much too ambitious, I believe.

3. How do we determine what is really a new cell type rather than either a refinement of an existing cell type generated by additional markers, or (2) a transient activation state of some known cell type? 3. How do we determine what is really a new cell type rather than either a refinement of an existing cell type generated by additional markers, or (2) a transient activation state of some known cell type? --> this should be the first question and the main focus of the Thur session *** I would rephrase the question as something like this: Can we list a set of properties that distinguish a cell type from a transient state? 4. How can we leverage CyTOF to develop a true step-by-step picture of hematopoiesis? This is a question for both ontology and the experimental approach. 4. How can we leverage CyTOF to develop a true step-by-step picture of hematopoiesis? This is a question for both ontology and the experimental approach. Too vague. How about: can we come up with an agreed process flow whereby at the end of the workflow we agree that a potential new cell type exists?


Schedule

8:30 Continental Breakfast
9:00 An Introduction to Ontology for CyTOF
9:30 An Introduction to Immunology for CyTOF
10:00 Immunology in the Gene Ontology (Alexander Diehl)
10:30 CL
11:00 PRO
12:00 Lunch
13:00 Consequences for the Future of Immunology Science (See questions above)
16:00 Close

Participants

  • Ryan Brinkman (Vancouver, BC)
  • Lindsay Cowell (UT Southwestern, Dallas)
  • Melanie Courtot (Vancouver, BC)
  • Alexander Diehl (ImmPort / Buffalo)
  • Sanda Harabagiu (UT Southwestern, Dallas)
  • Nikesh Kotecha (Stanford)
  • Yannick Pouliot (ImmPort / Stanford)
  • Alan Ruttenberg (ImmPort / Buffalo)
  • Ravi Shankar (ImmPort / Stanford)
  • Shai Shen-Orr (ImmPort / Technion Institute)
  • Barry Smith (ImmPort / Buffalo)

Plus participants from Stanford area